Osteoporosis makes bones weak and brittle, increasing the risk of fracture. In this paper, we designed a hybrid model to diagnose osteoporosis based on bone radiograph images. Two types of features were used to distinguish between the “healthy"and the “sick”. One type of features was obtained from deep convolutional neural networks (CNNs), named CNN features, and the other was hand-crafted features containing a group of standard texture features such as local binary pattern and gray level co-occurrence matrix and a group of “encoded features"that have shown impressive discriminative capabilities. We used a minimum-redundancy maximum-relevance algorithm to reduce the high dimensionality of the features and a support vector machine was used as the recognizer. This is the first study to fuse the CNNs features with the state-of-the-art osteoporotic texture features for osteoporosis diagnosis. We explore if the fusion of the two types of powerful features will increase the performance or not. Comparative experiments show that considerable performance improvements can be made through the fusion of both types of features, and the fusion of AlexNet with encoded features or all the hand-crafted features achieved the highest accuracy among all the fusions.